scholarly journals USE OF THE FUZZY CLUSTERING ALGORITHM FOR PATTERN RECOGNITION IN FEED CONSUMPTION DATA OF PURE NEW ZEALAND WHITE RABBITS EXPOSED TO VARIED THERMAL CHALLENGES

2020 ◽  
Vol 4 (2) ◽  
pp. 9-14
Author(s):  
Maria Alice Junqueira Gouvêa Silva ◽  
Tadayuki Yanagi Junior ◽  
Raquel Silva de Moura ◽  
Patrícia Ferreira Ponciano Ferraz ◽  
Bruna Pontara Vilas Boas Ribeiro ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
New Zealand ◽  
Clustering Algorithm ◽  
High Sensitivity ◽  
Feed Consumption ◽  
Related Factors ◽  
Fuzzy C Means ◽  
New Zealand White Rabbits ◽  
Fcm Clustering ◽  
Set Up

The performance of New Zealand White rabbits (NZW) is directly associated with to ambiance-related factors because they present high sensitivity to high-temperature conditions. The objective of the present work was to use the Fuzzy C-Means (FCM) clustering algorithm for pattern recognition in daily feed consumption (CDR) of NZW rabbits exposed to different thermal challenges. The experiment was carried out in four air-conditioned wind tunnels installed in a laboratory. Twenty-four pure rabbits of the NZW breed aged 30 to 37 days were used. The experiment was carried out in two stages with a period of seven days each, and, at each stage, four dry bulb temperatures (20°C, 24ºC, 28ºC and 32ºC) were tested from the 30th day of the rabbits’ life. Data on CDR (kilo, kg day-1) were obtained by weighing the quantities supplied and the leftovers obtained daily from each rabbit in each treatment. Afterward, the Fuzzy C-Means algorithm (FCM) was used to classify the results. Also, to validate the analysis, the validation indexes were applied to indicate in which quantities of clusters the best partition results were obtained for this database. Thus, FCM cluster analysis was set up as a methodology capable of providing information on the thermal comfort of NZB rabbits in a precise and non-invasive way, which could assist the producer in decision-making.

scholarly journals Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2344 ◽  
Author(s):  
Enwen Li ◽  
Linong Wang ◽  
Bin Song ◽  
Siliang Jian
Keyword(s):  
Fault Diagnosis ◽  
Membership Function ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Fuzzy C Means ◽  
Dissolved Gas Analysis ◽  
Fcm Clustering ◽  
Transformer Fault

Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

Weighting exponent m in fuzzy C-means (FCM) clustering algorithm

2001 ◽  
Author(s):  
Jihong Pei ◽  
Xuan Yang ◽  
Xinbo Gao ◽  
Weixing Xie
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy C Means ◽  
Fcm Clustering

Recognition of High-Voltage Cable Partial Discharge Signal Based on Adaptive Fuzzy C-Means Clustering

2017 ◽  
Vol 31 (06) ◽  
pp. 1759009 ◽  
Author(s):  
Baichao Chen ◽  
Weiqiang Qi ◽  
Jiaxin Yuan ◽  
Yihong You
Keyword(s):  
High Voltage ◽  
Clustering Algorithm ◽  
Phase Distribution ◽  
Partial Discharge ◽  
Effective Means ◽  
Time Frequency ◽  
Adaptive Fuzzy ◽  
Fuzzy C Means ◽  
Fcm Clustering ◽  
Interference Signals

Partial discharge (PD) detection is an effective means to find high-voltage cable defects. However, various interference signals affect the PD signal in practical applications, resulting in wrong judgment. In order to improve the accuracy of PD detection of high voltage cables, an adaptive fuzzy C-means (FCM) clustering was proposed to identify PD signals. The adaptive threshold pulse extraction algorithm based on fixed interval width was employed. The threshold was changed adaptively to extract the effective PD pulse waveform according to the change of the background noise and the degree of PD. Then PD pulse features were analyzed in time and frequency domain by employing the equivalent time frequency analysis method. The adaptive fuzzy clustering algorithm was used to classify the signals. The phase distribution concentration of all kinds of pulse signal was calculated. The phase standard deviation of various types of pulse was taken as the index that measures the concentration of density to distinguish between PD and interference signals. Results show that the adaptive FCM clustering algorithm, compared with the traditional method, can not only identify the PD signal accurately, but also be conscious of the PD category. The PD recognition method proposed in this paper has strong applicability and high accuracy, which is particularly suitable for application in the field of engineering.

scholarly journals Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2085
Author(s):  
Ranjita Rout ◽  
Priyadarsan Parida ◽  
Youseef Alotaibi ◽  
Saleh Alghamdi ◽  
Osamah Ibrahim Khalaf
Keyword(s):  
Skin Lesion ◽  
Clustering Algorithm ◽  
Skin Lesions ◽  
Gaussian Filter ◽  
Watershed Transform ◽  
Fuzzy C Means ◽  
Local Variance ◽  
Variance Filter ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

Early identification of melanocytic skin lesions increases the survival rate for skin cancer patients. Automated melanocytic skin lesion extraction from dermoscopic images using the computer vision approach is a challenging task as the lesions present in the image can be of different colors, there may be a variation of contrast near the lesion boundaries, lesions may have different sizes and shapes, etc. Therefore, lesion extraction from dermoscopic images is a fundamental step for automated melanoma identification. In this article, a watershed transform based on the fast fuzzy c-means (FCM) clustering algorithm is proposed for the extraction of melanocytic skin lesion from dermoscopic images. Initially, the proposed method removes the artifacts from the dermoscopic images and enhances the texture regions. Further, it is filtered using a Gaussian filter and a local variance filter to enhance the lesion boundary regions. Later, the watershed transform based on MMLVR (multiscale morphological local variance reconstruction) is introduced to acquire the superpixels of the image with accurate boundary regions. Finally, the fast FCM clustering technique is implemented in the superpixels of the image to attain the final lesion extraction result. The proposed method is tested in the three publicly available skin lesion image datasets, i.e., ISIC 2016, ISIC 2017 and ISIC 2018. Experimental evaluation shows that the proposed method achieves a good result.

A modified Fuzzy C-Means (FCM) Clustering algorithm and its application on carbonate fluid identification

2016 ◽  
Vol 129 ◽  
pp. 28-35 ◽  
Author(s):  
Lifeng Liu ◽  
Sam Zandong Sun ◽  
Hongyu Yu ◽  
Xingtong Yue ◽  
Dong Zhang
Keyword(s):  
Clustering Algorithm ◽  
Fluid Identification ◽  
Fuzzy C Means ◽  
Fcm Clustering

scholarly journals Clustering Batik Images using Fuzzy C-Means Algorithm Based on Log-Average Luminance

2012 ◽  
Vol 1 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Ahmad Sanmorino
Keyword(s):  
Clustering Algorithm ◽  
Staining Technique ◽  
Image Clustering ◽  
Fuzzy C Means ◽  
Average Value ◽  
Artistic Value ◽  
Fuzzy Models ◽  
Average Luminance ◽  
Fcm Clustering ◽  
Fuzzy C Means Algorithm

Batik is a fabric or clothes that are made ​​with a special staining technique called wax-resist dyeing and is one of the cultural heritage which has high artistic value. In order to improve the efficiency and give better semantic to the image, some researchers apply clustering algorithm for managing images before they can be retrieved. Image clustering is a process of grouping images based on their similarity. In this paper we attempt to provide an alternative method of grouping batik image using fuzzy c-means (FCM) algorithm based on log-average luminance of the batik. FCM clustering algorithm is an algorithm that works using fuzzy models that allow all data from all cluster members are formed with different degrees of membership between 0 and 1. Log-average luminance (LAL) is the average value of the lighting in an image. We can compare different image lighting from one image to another using LAL. From the experiments that have been made, it can be concluded that fuzzy c-means algorithm can be used for batik image clustering based on log-average luminance of each image possessed.DOI: 10.18495/comengapp.11.025031

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